DSA2101 Project: 2023 & 2024 US Solar Eclipses
Brian Bong Neng Ye (A0276127B), Mandy Yap Zhi Wei (A0276276R), Ong Jia Xi (A0276092Y), Lee Ang Xuan (A0276093X)
2024-11-15
Introduction
The 2023 & 2024 US Solar Eclipses dataset provides a comprehensive view of the solar eclipses observed across the United States on October 14, 2023, and April 8, 2024. An annular solar eclipse occurs when the Moon passes between the Sun and the Earth while at its farthest point from Earth. At this distance, the Moon appears smaller and cannot completely block the Sun, creating a “ring of fire” effect visible to those observers in areas within the path of annularity. In contrast, during a total solar eclipse, the Moon is closer to Earth, potentially blocking the Sun fully when positioned directly between the Sun and Earth. This creates a brief period of complete darkness for those located within the path of totality.
This dataset includes precise geographical information complete with
the longitude and latitude coordinates, names of towns and states as
well as timing and visibility details for each solar eclipse. Provided
by NASA’s Scientific Visualization Studio, the dataset comes in a CSV
file containing a total of four excel sheets: annularity of the
2023 eclipse, totality of the 2024 eclipse, and partial eclipse for both
2023 and 2024. Our research question focuses on
identifying the best locations for observing a solar
eclipse, considering both total and annular solar eclipses, as
well as areas with the longest duration of solar eclipses. To
investigate this, we utilised two of the excel sheets, which are
eclipse_annular_2023 with 811 entries and
eclipse_total_2024 with 3330 entries.
Overview of Datasets
First, we loaded the general libraries required.
# install.packages("ggplot2")
# install.packages("ggnewscale")
library(tidyverse)
library(tidytuesdayR)
library(ggplot2)
library(ggnewscale) # will be used for map and barplotNext, we loaded the datasets to begin our analysis.
eclipse_annular_2023 <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-04-09/eclipse_annular_2023.csv')
eclipse_total_2024 <- readr::read_csv('https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2024/2024-04-09/eclipse_total_2024.csv')Since the datasets were already clean, no general cleaning was
necessary. As we will deal with duration for every
visualisation, we first created a new column, duration, in
each dataset to calculate the eclipse duration between
eclipse_3 (time which totality/annularity starts) and
eclipse_4 (time which totality/annularity ends) in
minutes.
Then, we proceeded with the preprocessing steps used in each visualisation, as outlined below.
total_2024_with_duration <- eclipse_total_2024 %>%
mutate(duration = as.numeric(difftime(as.POSIXct(eclipse_4), as.POSIXct(eclipse_3), units = "mins")))
annul_2023_with_duration <- eclipse_annular_2023 %>%
mutate(duration = as.numeric(difftime(as.POSIXct(eclipse_4), as.POSIXct(eclipse_3), units = "mins")))To summarise the data, we computed some key statistics of the preprocessed data, focussing on the geographical locations of the eclipses and the duration of the eclipse.
annul_2023_stats <- annul_2023_with_duration %>%
summarise(
lat_min = min(lat, na.rm = TRUE),
lat_max = max(lat, na.rm = TRUE),
lon_min = min(lon, na.rm = TRUE),
lon_max = max(lon, na.rm = TRUE),
duration_min = min(duration, na.rm = TRUE),
duration_max = max(duration, na.rm = TRUE),
duration_mean = mean(duration, na.rm = TRUE),
duration_sd = sd(duration, na.rm = TRUE)
)
total_2024_stats <- total_2024_with_duration %>%
summarise(
lat_min = min(lat, na.rm = TRUE),
lat_max = max(lat, na.rm = TRUE),
lon_min = min(lon, na.rm = TRUE),
lon_max = max(lon, na.rm = TRUE),
duration_min = min(duration, na.rm = TRUE),
duration_max = max(duration, na.rm = TRUE),
duration_mean = mean(duration, na.rm = TRUE),
duration_sd = sd(duration, na.rm = TRUE)
)
print("Summary Statistics:")## [1] "Summary Statistics:"print(rbind("Annular 2023" = annul_2023_stats, "Total 2024" = total_2024_stats))## # A tibble: 2 × 8
## lat_min lat_max lon_min lon_max duration_min duration_max duration_mean
## * <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
## 1 27.2 44.9 -124. -96.7 0.0667 4.88 3.43
## 2 28.4 46.9 -101. -67.4 0.0333 4.48 3.08
## # ℹ 1 more variable: duration_sd <dbl>Based on the descriptive statistics, several key observations are noted. Although the two eclipses span similar latitude ranges, they cover significantly different ranges of longitudes. Additionally, the durations observed in the annular eclipse are notably shorter than those in the total eclipse.
To gain insights into our question, we utilised a variety of visualisations, including a map, bar plot, and scatter plot.
1. Map
A map visualisation is first used to identify the geographical
distribution of the eclipses and the paths they traverse. To illustrate
these paths, we utilize the geographical coordinates of each observation
point, specifically the longitude (lon) and latitude
(lat). Additionally, to examine the relationship between
the duration of each observed eclipse and its geographic location, each
observation point on the map is color-coded using a gradient scale based
on the duration of the eclipse observed.
We began by loading the necessary libraries.
## can uncomment these and run if needed
# install.packages("tigris")
# install.packages("sf")
# install.packages("rnaturalearth")
library(tigris)
library(sf)
library(rnaturalearth)Data Cleaning and Summary
Following that, we proceeded with additional preprocessing steps to ensure the datasets were ready for visualisation. This involved adjusting data types, and filtering necessary data for plotting.
us_map <- states(cb = TRUE, resolution = "20m", class = "sf")
total_2024 <- total_2024_with_duration %>%
st_as_sf(coords = c("lon", "lat"), crs = 4326)
annul_2023 <- annul_2023_with_duration %>%
st_as_sf(coords = c("lon", "lat"), crs = 4326)
texas <- us_map %>% filter(NAME == "Texas")
us_map_no_texas <- us_map %>% filter(NAME != "Texas")Visualisation
The variables used—lat, lon, and
duration were preprocessed in the ‘Data Cleaning and
Summary’ section above. This visualization offers a general view of how
both annular and total eclipses are distributed across the U.S. map.
From here, we can proceed to plot the map.
ggplot() +
geom_sf(data = us_map_no_texas, fill = "#2B2B2B", color = "#5A5A5A") +
geom_sf(data = texas, fill = "#40312f", color = "#5A5A5A") +
geom_sf(data = total_2024, aes(color = duration), size = 0.8, alpha = 0.5) +
scale_color_viridis_c(option = "mako", name = "Duration of Annular Eclipse on October 14, 2023 (min)",
labels = scales::comma,
guide = guide_colorbar(barwidth = 20, barheight = 1,
title.position = "top", title.hjust = 0.5,
title.theme = element_text(family = "Helvetica", size = 14))) +
new_scale_color() +
geom_sf(data = annul_2023, aes(color = duration), size = 0.8, alpha = 0.5) +
scale_color_viridis_c(option = "inferno", name = "Duration of Total Eclipse on April 8, 2024 (min)",
labels = scales::comma,
guide = guide_colorbar(barwidth = 20, barheight = 1,
title.position = "top", title.hjust = 0.5,
title.theme = element_text(family = "Helvetica", size = 14))) +
coord_sf(crs = st_crs("ESRI:102003"), xlim = c(-2300000, 2200000), ylim = c(-1400000, 1600000)) +
theme_minimal(base_family = "Arial") +
labs(title = "Path and Duration of Solar Eclipses Over the Continental United States",
subtitle = "Annular Eclipse on October 14, 2023 and Total Eclipse on April 8, 2024",
caption = "Data: NASA's Scientific Visualization Studio") +
annotate("text", x = -2400000, y = -1400000, label = "Each dot on the map represents \nan observation recorded in the town, with the colour \nrepresenting the total duration of the solar eclipse experienced.",
color = "#CCCCCC", family = "Helvetica", size = 5.5, hjust = 0) +
annotate("text", x = -500000, y = 900000, label = "Path of Annular Eclipse",
color = "white", family = "Helvetica", size = 5, hjust = 0.5) +
geom_curve(aes(x = -500000, y = 850000, xend = -1000000, yend = -10000),
color = "white", family = "Helvetica",
arrow = arrow(length = unit(0.3, "cm")), curvature = -0.1) +
annotate("text", x = 300000, y = 850000, label = "Path of Total Eclipse",
color = "white", family = "Helvetica", size = 5, hjust = 0.5) +
geom_curve(aes(x = 300000, y = 850000, xend = 500000, yend = 12000),
color = "white", family = "Helvetica",
arrow = arrow(length = unit(0.3, "cm")), curvature = 0.2) +
annotate("text", x = 300000, y = -1100000, label = "Note that both eclipses \noverlap over the state Texas.", color = "white", size = 5, hjust = 0) +
geom_curve(aes(x = 250000, y = -1100000, xend = 190000, yend = -920000), curvature = -0.3, color = "white") +
theme(
plot.background = element_rect(fill = "#1E1E1E", color = NA),
panel.background = element_rect(fill = "#1E1E1E", color = NA),
plot.title = element_text(size = 30, face = "bold", family = "Helvetica", color = "#EDEDED", hjust = 0.5, margin = margin(t = 10, b = 10)),
plot.subtitle = element_text(size = 20, color = "#EDEDED", family = "Helvetica", hjust = 0.5),
plot.caption = element_text(size = 20, color = "#9A9A9A", family = "Helvetica", hjust = 0.5),
legend.background = element_blank(),
legend.key = element_blank(),
legend.position = "bottom",
legend.spacing.x = unit(1, "cm"),
legend.title = element_text(size = 12, color = "#EDEDED", family = "Helvetica"),
legend.text = element_text(size = 15, color = "#EDEDED", family = "Helvetica"),
panel.grid = element_line(color = "#4A4A4A", linetype = "dashed")
) 
Discussion
Our map visualisation of the U.S. illustrates the path and duration
of eclipse_annular_2023 and
eclipse_total_2024. Each point on the map represents an
observation recorded in a town within its respective state. The
gradient of colours reflects the eclipse duration:
points shaded from black to yellow represent the
duration of eclipse_total_2024, while the points
shaded from black to light blue represent the duration of
eclipse_annular_2023. As the colour of the points changes
from black to a lighter shade—yellow for the total eclipse and
light blue for the annular eclipse—the duration of the eclipse
increases. The map features two intersecting
lines, with the line on the left representing the path of
eclipse_annular_2023 and the bar on the line representing
the path of eclipse_total_2024. In both lines, there is a
clear trend where colour brightness increases from the bar’s
edge toward its centre, indicating that locations at the centre
of the path would observe longer eclipses. Additionally, the
intersecting area of the two lines marks the state of Texas, meaning
that certain areas in Texas have experienced the annular eclipse in 2023
and will experience the total eclipse in 2024. We observe that certain
states have experienced longer eclipses - we would try to quantify such
observation in Visualisation 2.
2. Barplot
A barplot visualization is used to rank states by the average
duration of each eclipse observed within them. This approach allows us
to identify which states experience the longest eclipse durations on
average for both the annular and total eclipses. By comparing these
averages, the visualization also helps reveal any states that might be
positioned to observe both types of eclipses. Highlighting such states
could provide valuable insights into regions with optimal viewing
opportunities across both eclipse events. To visualise, we extract
information from state to identify the state in which the
observation is made, name of the town, and the
duration of the eclipse observed.
We began by loading the necessary libraries.
# install.packages("usa")
library(usa)Data Cleaning and Summary
For both eclipse_total_2024 and
eclipse_annular_2023 datasets, we selected the columns
state, name and duration.
Afterwards, we merged the two datasets, renamed and relocated certain columns, and prepared the data for plotting the graph.
total_2024 <- total_2024_with_duration %>%
select(state, name, duration) %>%
left_join(usa::states, by = c("state" = "abb")) %>%
select(state, duration, name.y) %>%
rename(state_name = name.y) %>%
relocate(state_name, .after = state)
annul_2023 <- annul_2023_with_duration %>%
select(state, name, duration) %>%
left_join(usa::states, by = c("state" = "abb")) %>%
select(state, duration, name.y) %>%
rename(state_name = name.y) %>%
relocate(state_name, .after = state)The variable used to create the bar plot is the mean duration of each eclipse, calculated for each state across both eclipses.
total_state_mean <- total_2024 %>%
group_by(state_name) %>%
summarise(mean_duration = mean(duration)) %>%
arrange(mean_duration)
annul_state_mean <- annul_2023 %>%
group_by(state_name) %>%
summarise(mean_duration = mean(duration)) %>%
arrange(desc(mean_duration))
state_order <- total_state_mean %>%
full_join(annul_state_mean, by = "state_name", suffix = c("_total", "_annul")) %>%
pull(state_name)
interval_size <- 0.01
# Create finer-grained data by expanding each observation into small intervals
df_plot <- total_state_mean %>%
full_join(annul_state_mean, by = "state_name", suffix = c(".total", ".annul")) %>%
mutate(
state_name = factor(state_name, levels = state_order),
mean_duration.total = ifelse(is.na(mean_duration.total), 0, mean_duration.total),
mean_duration.annul = ifelse(is.na(mean_duration.annul), 0, mean_duration.annul)
) %>%
pivot_longer(cols = starts_with("mean_duration"), names_to = "type", values_to = "duration") %>%
group_by(state_name, type) %>%
# Split each duration into smaller intervals
mutate(segment = list(seq(0, duration, by = interval_size))) %>%
unnest(segment) %>%
ungroup()
df_plot_total <- df_plot %>% filter(type == "mean_duration.total")
df_plot_annul <- df_plot %>% filter(type == "mean_duration.annul")Visualisation
The variables that we will be using are processed in our data
preprocessing section, which are state_name,
mean_duration.total, and mean_duration.annul.
This type of visualisation can give us the durations for total and
annular eclipses happened in each state so we can dive further into it
using the information given by this visualisation.
With the data prepared, we are now ready to proceed with plotting.
options(repr.plot.width = 7, repr.plot.height = 4.5)
ggplot(df_plot, aes(x = state_name)) +
# Total eclipse bars (left side) with gradient segments
geom_tile(data = df_plot_total,
aes(y = -segment, height = interval_size, fill = segment),
width = 0.8, show.legend = TRUE) +
scale_fill_viridis_c(option = "magma", name = "Total Eclipse Duration in 2024 (min)",
limits = c(0, 3.5), direction = -1) +
# Separate fill scale for annular eclipse
new_scale_fill() +
# Annular eclipse bars (right side) with gradient segments
geom_tile(data = df_plot_annul,
aes(y = segment, height = interval_size, fill = segment),
width = 0.8, show.legend = TRUE) +
scale_fill_viridis_c(option = "mako", name = "Annular Eclipse Duration in 2023 (min)",
limits = c(0, 4), direction = -1) +
scale_y_continuous(breaks = seq(-4, 4, by = 1), labels = abs(seq(-4, 4, by = 1))) +
coord_flip() +
labs(x = NULL, y = "Mean Duration (minutes)",
title = "Mean Duration of Annular Eclipse and Total Eclipse (Projection)",
subtitle = "... across different states in the United States in 2023 & 2024",
caption = "Data: NASA's Scientific Visualization Studio") +
theme_minimal(base_family = "AppleGothic") +
theme(
plot.background = element_rect(fill = "grey20", color = NA),
panel.background = element_rect(fill = "grey20", color = NA),
panel.grid.major = element_blank(), # Remove major grid lines
panel.grid.minor = element_blank(), # Remove minor grid lines
axis.text = element_text(color = "white", family = "Helvetica", size = 6),
axis.text.y = element_text(color = "white", family = "Helvetica", size = 8),
axis.title.x = element_text(color = "white", family = "Helvetica", size = 8),
plot.title = element_text(color = "white", face = "bold", family = "Helvetica", size = 10, hjust = 0.4),
plot.caption = element_text(color = "white", family = "Helvetica", size = 6),
plot.subtitle = element_text(color = "white", family = "Helvetica", size = 8, hjust = 0.29),
legend.position = "top",
legend.title = element_text(color = "white", family = "Helvetica", size = 6),
legend.text = element_text(color = "white", family = "Helvetica", size = 6)
) +
geom_segment(aes(x = 15.5, xend = 15.5, y = -2.75, yend = -3.25),
lineend = "round", linejoin = "bevel",
size = 1, arrow = arrow(length = unit(0.1, "inches")), color = viridis::magma(n=1, begin = 0.3)) +
geom_text(aes(x = 15.5, y = -1.5, label = "Increasing Total Eclipse Duration"), family = "Helvetica", color = viridis::magma(n=1, begin = 0.45), size = 3) +
geom_segment(aes(x = 10.5, xend = 10.5, y = 3, yend = 3.75),
lineend = "round", linejoin = "bevel",
size = 1, arrow = arrow(length = unit(0.1, "inches")), color = viridis::mako(n=1, begin = 0.6)) +
geom_text(aes(x = 10.5, y = 1.65, label = "Increasing Annular Eclipse Duration"), family = "Helvetica", color = viridis::mako(n=1, begin = 0.85), size = 3) +
geom_curve(aes(xend = 3.5, yend = 1.5, x = 11.5, y = 0.2), lineend = "round", linejoin = "bevel",
arrow = arrow(length = unit(0.02, "npc")),
size = 0.5, color = "white") +
annotate("text", x = 3.5, y = 1.6, label = "Texas is the only state in the US where\nannular eclipse and total eclipse can\nboth occur in 2023 and 2024 respectively.", size = 2.2, color = "white", family = "Helvetica", fontface = "bold", hjust = "left")
Discussion
In our bar plot visualisation, we showcased the mean
durations of annular and total solar eclipses across various states in
the United States in 2023 and 2024. The left side of the plot
displays the projected total eclipse duration for each state in
2024, with its mean duration increasing as the bar moves to the
left. Concurrently, the right side presents the mean annular
eclipse duration for each state from 2023, with increasing mean
duration as the bar extends further to the right. Notably,
Texas stands out as the only state where an annular eclipse
has happened in 2023 and a total eclipse is anticipated in 2024, making
it a prime destination for eclipse enthusiasts. In 2023, Texas
experienced one of the longest annularity durations, which lasted
approximately 3.76 minutes. The state is also projected to
have one of the longest totality durations in 2024 across all states,
lasting around 3.25 minutes. This phenomenon highlights
Texas as a unique location in the contiguous United States
for observing both types of solar eclipses, providing a rare and
unparalleled experience for those interested in celestial events.
3. Scatterplot
Having identified Texas as the ideal state for observing both
eclipses, we conduct a more detailed analysis by using a scatterplot to
pinpoint towns within Texas that would be well-suited for both
observations. The scatterplot visualizes each town’s geographic location
and the duration of the eclipses observed there, allowing us to assess
which towns offer optimal viewing conditions for both events. To conduct
the visualisation, we filtered the data by state to obtain
observations conducted in Texas, before using the
duration to plot the observations and the name
to label the observations.
Firstly, load the libraries required.
# install.packages("plotly") # can uncomment and run if needed
library(plotly)Data Cleaning and Summary
We filtered the data to focus on Texas and renamed the duration columns for consistency, ensuring a smooth join of the total and annular eclipse datasets.
abb_for_Texas <- state.abb[match("Texas", state.name)]
texas_total_2024 <-
total_2024_with_duration %>%
filter(state == abb_for_Texas) %>% # abbreviation for Texas
rename(total_duration = duration) %>%
select(name, lat, lon, total_duration)
texas_annul_2023 <-
annul_2023_with_duration %>%
filter(state == abb_for_Texas) %>% # abbreviation for Texas
rename(annul_duration = duration) %>%
select(name, lat, lon, annul_duration)Before joining both total eclipse data and annular eclipse data into a single dataframe for plotting the scatterplot, we first checked the uniqueness of the columns to determine the most efficient way to join them.
texas_annul_2023 %>%
count(lat, lon) %>%
filter(n > 1)## # A tibble: 0 × 3
## # ℹ 3 variables: lat <dbl>, lon <dbl>, n <int>texas_annul_2023 %>%
count(lat, lon) %>%
filter(n > 1)## # A tibble: 0 × 3
## # ℹ 3 variables: lat <dbl>, lon <dbl>, n <int>As shown, all lat and lon pairs are unique within the datasets. Therefore, these pairs can serve as indicator to join the dataframes if needed.
texas_annul_2023 %>%
count(name) %>%
filter(n > 1)## # A tibble: 0 × 2
## # ℹ 2 variables: name <chr>, n <int># as an example to check if the lat and lon are the same or not
texas_annul_2023 %>%
filter(name == "Chula Vista")## # A tibble: 0 × 4
## # ℹ 4 variables: name <chr>, lat <dbl>, lon <dbl>, annul_duration <dbl>texas_total_2024 %>%
count(name) %>%
filter(n > 1)## # A tibble: 1 × 2
## name n
## <chr> <int>
## 1 Chula Vista 2We can observe that the city in a state can contain at most 2 rows of data with different lat and lon pairs. Therefore, using only city names as a joining key is not sufficient.
After reviewing the datasets, we decided to join them according to name, lat and lon.
joined_total_annul <- texas_annul_2023 %>%
inner_join(texas_total_2024, by = c("name", "lat", "lon")) %>%
select(name, annul_duration, total_duration)Visualisation
The variables that we are using are processed in our data
preprocessing section, which are name,
total_duration, and annul_duration. This type
of visualisation can give us the longest durations for both total and
annular eclipses happening in every town in Texas so we can
find out the top destination for eclipse enthusiasts.
Finally, we can start with our plotting of scatterplot.
scatter_plot <- ggplot(data = joined_total_annul) +
geom_abline(color = "white") +
geom_point(mapping = aes(x = annul_duration, y = total_duration,
text = paste("Town: ", name, "<br>Annularity Duration: ",
round(annul_duration,2), "min", "<br>Totality Duration: ", round(total_duration,2), "min")),
size = 3,
color = "#FF9D00", #FF7600
alpha = 0.55) +
labs(title = "Relationship Between Duration of <br>2023 Annularity and 2024 Totality in Texas",
x = "Duration of Annularity in 2023 (min)",
y = "Duration of Totality in 2024 (min)") +
theme(plot.title = element_text(color = "#FFFDD0", family = "Helvetica", face = "bold", size=14, hjust = 0.5),
rect = element_rect(fill = "#222222", color = "white"),
axis.title = element_text(color = "#FFFDD0", family = "Helvetica", size=12),
axis.text = element_text(color = "#eaeaea"),
axis.line = element_line(color = "#969696", linetype = 1),
panel.background = element_rect(fill = "#222222", color = NULL),
panel.border = element_blank(),
panel.grid = element_line(color = "#1c1b1b"),
panel.grid.major = element_line(color = "#353535"),
plot.background = element_rect(fill = "#222222", colour = "#222222", linetype = 0))
ggplotly(scatter_plot, tooltip = "text") %>%
style(hoverlabel = list(font = list(family = "Helvetica", size = 14, color = "#222222"),
bgcolor = "#FFFDD0", # Set hover text background color
bordercolor = "black" # Set border color around hover text
))Discussion
Through our scatterplot visualisation, we focused on the state of
Texas to examine the relationship between the
durations of the 2023 annular and 2024 total solar eclipses. By
plotting observations across towns in Texas, we identified the best
towns with the longest durations for viewing both annular and total
solar eclipses, located at the most top right of the
plot. These towns are Leakey, which was recorded
to have 4.75 minutes of annular solar eclipse in
2023 and 4.34 minutes of total solar
eclipse in 2024, and Utopia, which was recorded to
have 4.85 minutes of annular solar eclipse in
2023 and 4.38 minutes of total solar
eclipse in 2024. Thus, this makes them ideal observation spots
for viewers who wish to maximise their experience of both types
of solar eclipses. We included an abline in
the scatterplot as a reference to help interpret the
relationship between the durations of both types of solar eclipses. The
diagonal line represents a situation where the duration of the 2023
annular eclipse is equal to the duration of the 2024 total eclipse.
Therefore, towns plotted close to this line experience relatively
balanced durations for both eclipse types. Deviations from the reference
line indicate towns where one type of eclipse has a longer duration than
the other.
Conclusion
Through the three visualisations—a map, bar plot, and
scatterplot—we conclude that the state of Texas is
the go-to destination for eclipse enthusiasts. It is
the only state where people can experience both types of solar
eclipses, annular and total, while also enjoying the longest
durations of each eclipse. In particular, the towns of
Utopia and Leakey in Texas offer the
best opportunities for observing both types of solar
eclipses, with the longest durations of full
solar eclipses recorded for each.
Teamwork
The project work was split evenly among the four members. Brian Bong Neng Ye preprocessed the data required and completed the map visualisation for the project. Mandy Yap Zhi Wei compiled the visualisations into a Markdown file and wrote the descriptions for the project, standardised aesthetic choices among charts. Ong Jia Xi preprocessed the data required and completed the barplot visualisation for the project. Lee Ang Xuan preprocessed the data required and completed the scatterplot visualisation for the project. Overall, the group found the work delegation satisfactory, with regular communication and ample discussion space within the group.
References
2023 & 2024 US Solar Eclipses, NASA’s Scientific Visualization Studio, https://github.com/rfordatascience/tidytuesday/blob/master/data/2024/2024-04-09/readme.md